B Cells droplet

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library(Seurat)
library(tidyverse)
library(here)
source(here('vignettes', 'bcells', 'common.R'))

Parsed with column specification:
cols(
  `MGI Gene/Marker ID` = col_character(),
  Symbol = col_character(),
  Name = col_character(),
  Chr = col_character(),
  Qualifier = col_character(),
  `Annotated Term` = col_character(),
  Context = col_character(),
  Proteoform = col_character(),
  Evidence = col_character(),
  `Inferred From` = col_character(),
  `Reference(s)` = col_character(),
  category = col_character()
)

load(here('vignettes', 'bcells', 'droplet_cd19.Robj'))

cluster.markers = FindAllMarkers(object = tiss_droplet_cd19, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

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cluster.markers %>% group_by(cluster) %>% top_n(-10, p_val_adj)

write_markers(cluster.markers, here('vignettes', 'bcells', 'droplet_cluster_markers.csv'))

top_cluster_markers = cluster.markers %>% group_by(cluster) %>% top_n(-10, p_val_adj)

nCol = 4
plots = FeaturePlot(tiss_droplet_cd19, features.plot = sort(unique(top_cluster_markers$gene)), 
                    do.return = TRUE, nCol = nCol, no.axes=TRUE, 
                    # Light grey to 
                    cols.use=c('lightgrey', '#008080'))

for (p in plots){
  gene_name = p$labels$title
  gene_tissues = top_organ_markers %>% filter(gene == p$labels$title) %>% select(tissue)
  gene_tissues = as.character(as.vector(gene_tissues$tissue))
  print(paste(gene_name, paste(gene_tissues)))
  title = paste0(gene_name, ' (', paste(gene_tissues, sep=', '), ')')
  p = p + labs(title=title)
  plots[[gene_name]] = p
}

Error in eval(lhs, parent, parent) : object 'top_organ_markers' not found

rr organ_markers = find_markers(tiss_droplet_cd19, ‘tissue’) head(organ_markers)

Filter only for tissues with enough cells

rr tissue_cell_counts = table(tiss_droplet_cd19@meta.data$tissue) print(tissue_cell_counts)

tissues_with_enough_cells = tissue_cell_counts[tissue_cell_counts > 50] tissues_with_enough_cells

rr organ_markers_enough_cells = filter(organ_markers, tissue %in% names(tissues_with_enough_cells)) organ_markers_enough_cells

rr top_organ_markers = organ_markers_enough_cells %>% group_by(tissue) %>% top_n(-10, p_val_adj) top_organ_markers

rr write_csv(organ_markers_enough_cells, here(‘vignettes’, ‘bcells’, ‘droplet_tissue_markers.csv’))

rr ggplot(data=organ_markers_enough_cells, aes(x=p_val)) + geom_histogram() + facet_grid(~tissue)

rr ggplot(data=organ_markers_enough_cells, aes(x=p_val_adj)) + geom_histogram() + facet_grid(~tissue)

rr top_organ_markers %>% filter(gene == p\(labels\)title) %>% select(tissue)

rr nCol = 4 plots = FeaturePlot(tiss_droplet_cd19, features.plot = sort(unique(top_organ_markers\(gene)), do.return = TRUE, nCol = nCol, no.axes=TRUE, # Light grey to cols.use=c('lightgrey', '#008080')) for (p in plots){ gene_name = p\)labels\(title gene_tissues = top_organ_markers %>% filter(gene == p\)labels\(title) %>% select(tissue) gene_tissues = as.character(as.vector(gene_tissues\)tissue)) print(paste(gene_name, paste(gene_tissues))) title = paste0(gene_name, ‘(’, paste(gene_tissues, sep=‘,’), ‘)’) p = p + labs(title=title) plots[[gene_name]] = p } plots.combined <- plot_grid(plotlist = plots, ncol = nCol) invisible(x = lapply(X = plots.combined, FUN = print)) ggsave(‘droplet_featureplot_top_organ_markers.pdf’, height = 40, width=15)